Semiconductor wafer fabrication equipment typically processes product wafers only 30% of the equipment's total available time (i.e., the overall equipment effectiveness (OEE) is 30%). Test wafers, scheduled and unscheduled downtime, setup, and idleness due to the lack of product or operator and other factors represent the other 70%. The use of advanced fault detection systems can improve OEE by reducing the 15% of unscheduled down time, 3% of scheduled downtime, 8% for test wafer, and 10% for setup. In current fault detection systems, a training set of tool-state data (flows, pressures, temperatures, etc.) and process-state data (optical emission, plasma power, etc.) are collected during normal operation. By collecting these data for normal runs, the tool-state and process-state data in subsequent runs can be used to determine departures from normal operation, and hence to detect a fault. While such systems have proven valuable, changes in fabrication tool components, variations in tool properties resulting from use (deposits, parts wear, etc.) or overhaul, or variations in the optical emission window, can change the range of acceptable data in normal operation. This in turn can result in false alarms, or undetected faults.
Fourier transform infrared (FTIR) spectrometry has been shown to be an ideal technique for quantitative analysis of complex mixtures of gases. The technique has been successfully used to monitor perfluorocarbon process emissions during in-situ plasma cleaning of a plasma enhanced CVD tool. (See Zazzera, L., and Reagen, W., "PFC Process Emissions Monitoring using Extractive FT-IR," in A Partnership for PFC Emissions Reductions, presented at SEMICON Southwest 96, pp 55-71, (1996); and Zazzera, L., Reagen, W., and Mahal, P., "Process Emissions Monitoring During C.sub.3 F.sub.8 CVD Chamber Cleaning using FT-IR" in A Partnership for PFC Emissions Reductions, presented at SEMICON Southwest 96, pp 81-85, (1996)). Multi-component gas mixtures of CF.sub.4, C.sub.2 F.sub.6, C.sub.3 F.sub.8, SiF.sub.4, COF.sub.2, and HF were simultaneously identified and quantified using extractive FTIR spectroscopy. Automated spectral analysis software now available enables the interpretation and quantification of the complex data set required for complete on-line analysis and control. Similar spectral analysis improvements have been developed for other gas sensors such as, for example, quadrapole mass spectrometers.